BARN:Behavior-Aware Relation Network for multi-label behavior detection in socially housed macaques

Quantification of behaviors in macaques provides crucial support for various scientific disciplines,including pharmacology,neuroscience,and ethology.Despite recent advancements in the analysis of macaque behavior,research on multi-label behavior detection in socially housed macaques,including consideration of interactions among them,remains scarce.Given the lack of relevant approaches and datasets,we developed the Behavior-Aware Relation Network(BARN)for multi-label behavior detection of socially housed macaques.Our approach models the relationship of behavioral similarity between macaques,guided by a behavior-aware module and novel behavior classifier,which is suitable for multi-label classification.We also constructed a behavior dataset of rhesus macaques using ordinary RGB cameras mounted outside their cages.The dataset included 65?913 labels for19 behaviors and 60?367 proposals,including identities and locations of the macaques.Experimental results showed that BARN significantly improved the baseline SlowFast network and outperformed existing relation networks.In conclusion,we successfully achieved multilabel behavior detection of socially housed macaques with both economic efficiency and high accuracy.

Fuzzy Logic Based UAV Suspicious Behavior Detection

In recent years,unmanned air vehicles(UAVs)are widely used in many military and civilian applications.With the big amount of UAVs operation in air space,the potential security and privacy problems are arising.This can lead to consequent harm for critical infrastructure in the event of these UAVs being used for criminal or terrorist purposes.Therefore,it is crucial to promptly identify the suspicious behaviors from the surrounding UAVs for some important regions.In this paper,a novel fuzzy logic based UAV behavior detection system has been presented to detect the different levels of risky behaviors of the incoming UAVs.The heading velocity and region type are two input indicators proposed for the risk indicator output in the designed fuzzy logic based system.The simulation has shown the effective and feasible of the proposed algorithm in terms of recall and precision of the detection.Especially,the suspicious behavior detection algorithm can provide a recall of 0.89 and a precision of 0.95 for the high risk scenario in the simulation.

Image sequence-based risk behavior detection of power operation inspection personnel

A novel image sequence-based risk behavior detection method to achieve high-precision risk behavior detection for power maintenance personnel is proposed in this paper.In this method,the original image sequence data is first separated from the foreground and background.Then,the free anchor frame detection method is used in the foreground image to detect the personnel and correct their direction.Finally,human posture nodes are extracted from each frame of the image sequence,which are then used to identify the abnormal behavior of the human.Simulation experiment results demonstrate that the proposed algorithm has significant advantages in terms of the accuracy of human posture node detection and risk behavior identification.

Detection of Nonlinear Behavior Induced by Hard Limiting in Voltage Source Converters in Wind Farms Based on Higher-order Spectral Analysis

In recent years, sub-synchronous oscillation accidents caused by wind power integration have received extensive attention. The recorded constant-amplitude waveforms can be induced by either linear or nonlinear oscillation mechanisms. Hence, the nonlinear behavior needs to be distinguished prior to choosing the analysis method. Since the 1960s, the higher-order statistics(HOS) theory has become a powerful tool for the detection of nonlinear behavior(DNB) in production quality control wherein it has mainly been applied to mechanical condition monitoring and fault diagnosis. This study focuses on the hard limiters of the voltage source converter(VSC) control systems in the wind farms and attempts to detect the nonlinear behavior caused by bi-or uni-lateral saturation hard limiting using the HOS analysis. First, the conventional describing function is extended to obtain the detailed frequency domain information on the bi-and uni-lateral saturation hard limiting. Furthermore, the bi-and tri-spectra are introduced as the HOS, which are extended into bi-and tri-coherence spectra to eliminate the effects of the linear parts on the harmonic characteristics of hard limiting in the VSC control system, respectively. The effectiveness of the HOS in the DNB and the classification of the hard-limiting types is proven, and its detailed derivation and estimation procedure is presented. Finally, the quadratic and cubic phase coupling in the signals is illustrated, and the performance of the proposed method is evaluated and discussed.

Review of Some Advances and Applications in Real-time High-speed Vision: Our Views and Experiences

The frame rate of conventional vision systems is restricted to the video signal formats （e.g., NTSC 30 fps and PAL 25 fps） that are designed on the basis of the characteristics of the human eye, which implies that the processing speed of these systems is limited to the recognition speed of the human eye. However, there is a strong demand for real-time high-speed vision sensors in many application fields, such as factory automation, biomedicine, and robotics, where high-speed operations are carried out. These high-speed operations can be tracked and inspected by using high-speed vision systems with intelligent sensors that work at hundreds of Hertz or more, especially when the operation is difficult to observe with the human eye. This paper reviews advances in developing real-time high Speed vision systems and their applications in various fields, such as intelligent logging systems, vibration dynamics sensing, vision-based mechanical control, three-dimensional measurement/automated visual inspection, vision-based human interface, and biomedical applications.

An Infrared Touch System for Automatic Behavior Monitoring

Key requirements of successful animal behavior research in the laboratory are robustness,objectivity,and high throughput,which apply to both the recording and analysis of behavior.Many automatic methods of monitoring animal behavior meet these requirements.However,they usually depend on high-performing hardware and sophisticated software,which may be expensive.Here,we describe an automatic infrared behavior-monitor(AIBM)system based on an infrared touchscreen frame.Using this,animal positions can be recorded and used for further behavioral analysis by any PC supporting touch events.This system detects animal behavior in real time and gives closed-loop feedback using relatively low computing resources and simple algorithms.The AIBM system automatically records and analyzes multiple types of animal behavior in a highly efficient,unbiased,and low-cost manner.